Depth images from depth cameras are increasingly used to detect people and objects in scenes for many applications such as to find positions of human or animal body-part centers, to find positions of objects in scenes and for other purposes such as medical image analysis. Processing the depth images is typically computationally expensive and time consuming.
Finding positions of human or animal body-part centers in image data such as depth images, color video images and other types of images may be useful in many application domains such as augmented reality, immersive gaming, human computer interaction and others. In many of these application domains body-part center positions are to be predicted in real-time and often the available image data may be noisy or incomplete. In some cases the computing resources available may comprise graphics processing units that are operable in parallel to give fast processing times. However, this is not always the case. There is a need to reduce the amount of computation without significantly impacting accuracy and usability of the resulting body-part center positions.
Existing body-part position detection systems may involve several stages of computation. Typically a detection system is trained in advance using labeled image data.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known depth image compression systems.